The effect of silica thickness on nano TiO<sub>2</sub> particles for functional polyurethane nanocomposites
Bibliographic record
Abstract
Abstract In order to help reduce the agglomeration of TiO 2 nanoparticles in polyurethane coatings while enhancing their photoactivity and mechanical/physical properties, this work examined encapsulating TiO 2 nanoparticles in a thin layer of SiO 2 , prior to their nanocomposite polymerization. By applying a Stöber process, varying thicknesses of SiO 2 were successfully coated onto the surface of anatase and rutile TiO 2 nanoparticles. The methylene blue results showed that different loadings of SiO 2 onto the TiO 2 surface significantly influenced their photocatalytic activity. When the loading weight of SiO 2 was lower than 3.25 wt%, the photocatalytic activity was enhanced, while with higher loadings, it gave lower photocatalytic activity. When the rutile phase TiO 2 surface was fully covered with SiO 2 , an enhanced photocatalytic activity was observed. When these silica coated nanoparticles were applied in polyurethane coatings, increasing the amount of SiO 2 on the titania surface increased the coatings contact angle from 75° to 87° for anatase phase and 70°–78° for rutile phase. The Young’s modulus was also increased from 1.06 GPa to 2.77 GMPa for anatase phase and 1.06–2.17 GPa for rutile phase, attributed to the silica layer giving better integration. The thermal conductivity of the polyurethane coatings was also successfully decreased by encapsulating SiO 2 on the titania surface for next generation high performance coatings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".